
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import json
import seaborn as sns
import time


os.getcwd()
pd.set_option('display.max_columns', None)


df = pd.read_json('data-sample.json')
df


# Function to flatten a dictionary column
def flatten_dict_column(df, column_name):
    if isinstance(df[column_name].iloc[0], dict): #check if column is dict or not by check the first row
        flattened_df = pd.json_normalize(df[column_name]) #function to flattened json to tabular data 
        #drop the  dict column and replace it with flattened columns 
        return pd.concat([df.drop(column_name, axis=1),  #axis 1 = drop columns that consist dict
                            flattened_df], axis=1) #axis 1 = concate the columns from flatened_df to df
    else:
        return df


pd.json_normalize(df['cod']) #function to flattened json to tabular data


#Flattened Dict Columns
df.columns
for c in df.columns:
    column_name = c
    df = flatten_dict_column(df, column_name)

df


df.columns

Index(['taskCreatedTime', 'taskAssignedTo', 'taskCompletedTime', 'taskStatus',
       'flow', 'taskId', 'lon', 'lat', 'amount', 'received', 'branch_dest',
       'taskStatusLabel', 'receiver_city', 'taskDetailStatusLabel',
       'taskDetailStatus', 'weight', 'branch_origin', 'taskStatus'],
      dtype='object')


#Change duplicate columns name and add on specific column name
df.columns = ['taskCreatedTime', 'taskAssignedTo', 'taskCompletedTime', 'taskStatus',
       'flow', 'taskId', 'lon', 'lat', 'cod_amount', 'cod_received', 'branch_dest',
       'taskStatusLabel', 'receiver_city', 'taskDetailStatusLabel',
       'taskDetailStatus', 'weight', 'branch_origin', 'taskStatus_1']


df.dtypes

taskCreatedTime           object
taskAssignedTo            object
taskCompletedTime         object
taskStatus                object
flow                      object
taskId                    object
lon                      float64
lat                      float64
cod_amount               float64
cod_received              object
branch_dest               object
taskStatusLabel           object
receiver_city             object
taskDetailStatusLabel     object
taskDetailStatus          object
weight                    object
branch_origin             object
taskStatus_1              object
dtype: object


df.isna().sum()

taskCreatedTime             0
taskAssignedTo              1
taskCompletedTime         768
taskStatus                  0
flow                        0
taskId                      0
lon                       768
lat                       768
cod_amount               5976
cod_received             5976
branch_dest                 0
taskStatusLabel           762
receiver_city              52
taskDetailStatusLabel     762
taskDetailStatus          762
weight                      0
branch_origin             293
taskStatus_1              762
dtype: int64


#change to numeric
df['weight'] = pd.to_numeric(df['weight'])


df.dtypes

taskCreatedTime           object
taskAssignedTo            object
taskCompletedTime         object
taskStatus                object
flow                      object
taskId                    object
lon                      float64
lat                      float64
cod_amount               float64
cod_received              object
branch_dest               object
taskStatusLabel           object
receiver_city             object
taskDetailStatusLabel     object
taskDetailStatus          object
weight                   float64
branch_origin             object
taskStatus_1              object
dtype: object


# df['taskCreatedTime_conv'] = pd.to_datetime(df['taskCreatedTime'],errors='coerce').dt.tz_convert('UTC')
# df['taskCompletedTime_conv'] = pd.to_datetime(df['taskCompletedTime'], errors='coerce', utc= True).dt.tz_convert('UTC')


# from pandas_profiling import ProfileReport
# profile = ProfileReport(df, title="MileApp")
# profile

df


df.isna().sum()

taskCreatedTime             0
taskAssignedTo              1
taskCompletedTime         768
taskStatus                  0
flow                        0
taskId                      0
lon                       768
lat                       768
cod_amount               5976
cod_received             5976
branch_dest                 0
taskStatusLabel           762
receiver_city              52
taskDetailStatusLabel     762
taskDetailStatus          762
weight                      0
branch_origin             293
taskStatus_1              762
dtype: int64


df["cod_received"].value_counts()

False    1663
True      695
Name: cod_received, dtype: int64


#Impute on NaN value for COD columns
df[(df["cod_received"].isna()) & (df["taskStatus"]=='done')]


#SEE what happened to an 
df[df['taskCompletedTime'].isnull() & df['taskDetailStatusLabel'].isnull() ]


df.sort_values(by=['taskCompletedTime'] ,ascending=True).loc[~df['taskDetailStatus'].isnull()]


# Lat and Long

plt.scatter(df['lon'], df['lat'])
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.title('Delivery Locations')
plt.show()


print(f'Total NA value on column Lon and Lat is {df["lon"].isna().sum()}, {df["lat"].isna().sum()}')

Total NA value on column Lon and Lat is 768, 768


#print lot Lan that is (0,0)
((df["lat"] == 0) & (df["lon"] == 0)).value_counts()

False    5883
True     2451
dtype: int64


'''There is Outlier on Lat Lon showed (0,0), Need to do more analysis'''
df.sort_values(by=['taskCompletedTime'] ,ascending=True).loc[df['taskCompletedTime'].isnull()]


sns.scatterplot(data=df, x='lon', y='lat', hue='taskStatusLabel')
plt.title('Geographic Distribution of Tasks')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()


'''
Case when Failed Exist on several location
'''

'\nCase when Failed Exist on several location\n'


# Filter the DataFrame where latitude and longitude are both 0
filtered_data = df[(df['lat'] == 0) & (df['lon'] == 0)]

# Create a count plot
plt.figure(figsize=(10, 6))
sns.countplot(data=filtered_data, x='taskStatusLabel')
plt.xlabel('Task Status Label')
plt.ylabel('Count')
plt.title('Count of Task Status Label for Latitude and Longitude = 0')
plt.xticks(rotation=45)
plt.show()


df[(df['lat'] == 0) & (df['lon'] == 0) & (df["taskStatusLabel"]=="Success")]


lonlat0 = df[(df['lat'] == 0) & (df['lon'] == 0)]

cross_tab = pd.crosstab(index=lonlat0['taskDetailStatusLabel'], columns=lonlat0['taskStatusLabel'])

plt.figure(figsize=(15, 10))
sns.heatmap(cross_tab, annot=True, cmap='Blues',fmt='d')
plt.xlabel('Task Status Label')
plt.ylabel('Task Detail Status Label')
plt.title('Counts of Task Status Label and Task Detail Status Label for Latitude=0 and Longitude=0',fontsize = 16)
plt.show()


lotlan0 = df[(df['lat'] == 0) & (df['lon'] == 0) & (df["taskStatusLabel"]=="Failed")]
lotlan0["taskDetailStatusLabel"].value_counts()

MISROUTE                                             338
NEW ADDRESS                                          186
RUMAH service/ KANTOR KOSONG (MASIH DIHUNI)           92
ALAMAT TIDAK LENGKAP service/ TIDAK DIKENAL           81
FORCE MAJEURE                                         42
DIAMBIL SENDIRI                                       23
DITOLAK OLEH PENERIMA                                 23
TUTUP PADA AKHIR PEKAN service/ HARI LIBUR            21
PENERIMA TIDAK DIKENAL                                14
HOLD FOR FURTHER INSTRUCTI0N                          14
TUTUP/LIBUR CUTI/DINAS LUAR KOTA (KIRIMAN COD)        12
PENERIMA MENOLAK BAYAR (KIRIMAN COD)                   9
MENUNGGU PEMBAYARAN COD                                8
PENERIMA MENOLAK MENERIMA KIRIMAN COD (TDK PESAN)      4
PENERIMA PINDAH ALAMAT                                 3
Name: taskDetailStatusLabel, dtype: int64


value_counts = lotlan0["taskDetailStatusLabel"].value_counts()

plt.figure(figsize=(10, 6))
value_counts.plot.bar()
plt.xlabel('Task Detail Status Label')
plt.ylabel('Count')
plt.title('Value Counts of Task Detail Status Label for Lotitude=0, Longitude=0, and Task Status Label=Failed')
plt.show()


df["taskStatus"].value_counts()

done       7572
ongoing     762
Name: taskStatus, dtype: int64


df["taskStatusLabel"].value_counts()

Success    5427
Failed     2145
Name: taskStatusLabel, dtype: int64


df.isna().sum()

taskCreatedTime             0
taskAssignedTo              1
taskCompletedTime         768
taskStatus                  0
flow                        0
taskId                      0
lon                       768
lat                       768
cod_amount               5976
cod_received             5976
branch_dest                 0
taskStatusLabel           762
receiver_city              52
taskDetailStatusLabel     762
taskDetailStatus          762
weight                      0
branch_origin             293
taskStatus_1              762
dtype: int64


## input Nan lot lan with 0
df[df["lat"].isna()]


#Input Nan Value on lat lon with 0
df['lat'] = df['lat'].fillna(0)
df['lon'] = df['lon'].fillna(0)
df


# Get the value counts of 'cod_received'
cod_received_counts = df["cod_received"].value_counts()

# Plot the value counts
plt.figure(figsize=(8, 6))
sns.barplot(x=cod_received_counts.index, y=cod_received_counts.values)
plt.xlabel('COD Received')
plt.ylabel('Count')
plt.title('Value Counts of COD Received')
plt.show()


#check null
df["cod_received"].isna().sum()

5976


# Impute Nan on cod_received
df['cod_received'].fillna('No applicable', inplace=True)


# Create a cross-tabulation of 'cod_received', 'taskStatusLabel', and 'taskDetailStatusLabel'
cross_tab = pd.crosstab(index=[df['cod_received'], df['taskStatusLabel']], columns=df['taskDetailStatusLabel'])

# Create a heatmap of the cross-tabulation
plt.figure(figsize=(15, 10))
sns.heatmap(cross_tab, annot=True, cmap='Pastel2', fmt='d', linewidths=0.5)

plt.xlabel('Task Detail Status Label')
plt.ylabel('COD Received - Task Status')
plt.title('Relationship between COD Received, Task Status, and Task Detail Status Label')
plt.show()


df[(df["cod_received"].isna()) & (df["taskStatus"]=='done')]


import seaborn as sns
import matplotlib.pyplot as plt

# Create a cross-tabulation table
cross_tab = pd.crosstab(df['cod_received'], df['taskStatusLabel'].isna())

# Plot the cross-tabulation table as a heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(cross_tab, annot=True, cmap='Blues', fmt="d")
plt.xlabel('Task Status Label is NA')
plt.ylabel('COD Recieved Status ')
plt.title('Correlation: COD Recieved Status vs Task Status Label NA')
plt.show()


plt.figure(figsize=(10, 6))
sns.histplot(data=df, x='cod_amount', bins=50, kde=True)
plt.axvline(df['cod_amount'].mean(), color='red', linestyle='--', linewidth=2, label='Mean')
plt.xlabel('COD Amount')
plt.ylabel('Count')
plt.title('Distribution of COD Amount')
plt.legend()

# Add text annotation for the mean value
mean_value = df['cod_amount'].mean()
plt.text(0.95, plt.ylim()[1]*0.9, f'Mean: {mean_value:.2f}', color='red', ha='left', va='top')

plt.show()


plt.figure(figsize=(10, 6))
sns.boxplot(data=df, y='cod_amount')
plt.ylabel('COD Amount')
plt.title('Box Plot of COD Amount')
plt.show()


# Box plot of 'cod_amount' by 'taskStatus'
plt.figure(figsize=(10, 6))
sns.boxplot(data=df, x='taskStatusLabel', y='cod_amount')
plt.xlabel('Task Status')
plt.ylabel('COD Amount')
plt.title('COD Amount by Task Status Label')
plt.show()


df.groupby('taskStatusLabel')['cod_amount'].quantile([0.25, 0.5, 0.75,0.98]).unstack()


df.groupby('taskStatusLabel')['cod_amount'].mean()

taskStatusLabel
Failed     200683.561134
Success    139152.680650
Name: cod_amount, dtype: float64


cod_q98 = df[(df['cod_amount'] > 757670) & (df['taskStatusLabel'] == 'Failed')]


import seaborn as sns
import matplotlib.pyplot as plt

# Get the count of each task status
status_counts = df['taskStatus'].value_counts()

# Create a countplot
plt.figure(figsize=(8, 6))
sns.countplot(data=df, x='taskStatus')
plt.xlabel('Task Status')
plt.ylabel('Count')
plt.title('Distribution of Task Status')

# Add count values above each bar
for i, count in enumerate(status_counts):
    plt.text(i, count, str(count), ha='center', va='bottom')

plt.show()


df['taskStatus'].value_counts()

done       7572
ongoing     762
Name: taskStatus, dtype: int64


df['taskDetailStatusLabel'].value_counts()

YANG BERSANGKUTAN                                    3109
KELUARGA/SAUDARA                                      774
MISROUTE                                              763
ATASAN/STAFF/KARYAWAN/BAWAHAN                         634
SECURITY                                              564
ALAMAT TIDAK LENGKAP service/ TIDAK DIKENAL           322
RUMAH service/ KANTOR KOSONG (MASIH DIHUNI)           304
NEW ADDRESS                                           247
DIAMBIL SENDIRI                                       100
SUAMI/ISTRI/ANAK                                       94
RECEPTIONIST                                           87
TUTUP PADA AKHIR PEKAN service/ HARI LIBUR             70
PENERIMA TIDAK DIKENAL                                 64
MAILING ROOM                                           62
PEMBANTU                                               61
DITOLAK OLEH PENERIMA                                  52
PENERIMA MENOLAK BAYAR (KIRIMAN COD)                   48
PENERIMA PINDAH ALAMAT                                 45
FORCE MAJEURE                                          42
MENUNGGU PEMBAYARAN COD                                27
HOLD FOR FURTHER INSTRUCTI0N                           24
PENJAGA KOS                                            21
PENERIMA MENOLAK MENERIMA KIRIMAN COD (TDK PESAN)      17
TUTUP/LIBUR CUTI/DINAS LUAR KOTA (KIRIMAN COD)         13
SUPIR                                                  11
OFFICE BOY                                              6
SEKRETARIS                                              4
RUMAH service/ KANTOR TIDAK DIHUNI                      3
MENUNGGU KONFIRMASI NILAI COD                           2
CRISS-CROSS                                             1
DAMAGE CASE                                             1
Name: taskDetailStatusLabel, dtype: int64


# Calculate the frequency counts of each category
category_counts = df['taskDetailStatusLabel'].value_counts()

# Sort the categories based on their counts in descending order
sorted_categories = category_counts.index.tolist()

# Create a count plot of 'taskDetailStatusLabel' with sorted categories
plt.figure(figsize=(12, 8))
sns.countplot(data=df, y='taskDetailStatusLabel', order=sorted_categories)
plt.ylabel('Task Detail Status Label')
plt.xlabel('Count')
plt.title('Distribution of Task Detail Status Labels')

# Increase the rotation angle of the y-axis labels
plt.xticks(rotation=90)

# Adjust spacing to prevent overlapping labels
plt.tight_layout()

plt.show()


# Create a cross-tabulation of 'taskStatusLabel' and 'taskDetailStatusLabel'
cross_tab = pd.crosstab(df['taskStatusLabel'], df['taskDetailStatusLabel'])

# Create a heatmap of the cross-tabulation
plt.figure(figsize=(14, 10))
sns.heatmap(cross_tab, annot=True, cmap='Pastel2', fmt='d', linewidths=0.5)
plt.xlabel('Task Detail Status Label')
plt.ylabel('Task Status')
plt.title('Relationship between Task Status Label and Task Detail Status Label')
plt.show()


'''
Most FAILED case is by Missroute, Rumah/kantor/TIDAK DIHUNI, NEW ADDRESS

'''

'\nMost FAILED case is by Missroute, Rumah/kantor/TIDAK DIHUNI, NEW ADDRESS\n\n'


df['cod_received'].value_counts()

No applicable    5976
False            1663
True              695
Name: cod_received, dtype: int64


df['cod_received'].isna().sum()

0


df['taskStatusLabel'].value_counts()

Success    5427
Failed     2145
Name: taskStatusLabel, dtype: int64


# Create a cross-tabulation of 'cod_received', 'taskStatus', and 'taskDetailStatusLabel'
cross_tab = pd.crosstab(index=[df['cod_received'], df['taskStatusLabel']], columns=df['taskDetailStatusLabel'])

# Create a heatmap of the cross-tabulation
plt.figure(figsize=(12, 8))
sns.heatmap(cross_tab, annot=True, cmap='Pastel2', fmt='d', linewidths=0.5)
plt.xlabel('Task Detail Status Label')
plt.ylabel('COD Received - Task Status')
plt.title('Relationship between COD Received, Task Status, and Task Detail Status Label', fontsize =10)
plt.show()


# Create a histogram
plt.figure(figsize=(10, 6))
sns.boxplot(data=df, y='weight')
plt.xlabel('Weight')
plt.ylabel('Count')
plt.title('Distribution of Weight')
plt.show()


# df.groupby('taskStatusLabel')['cod_amount'].quantile([0.25, 0.5, 0.75,0.98]).unstack()
df['weight'].quantile([0.25, 0.5, 0.75,0.99])

0.25     1.0000
0.50     1.0000
0.75     1.6000
0.99    22.8878
Name: weight, dtype: float64


df[df["weight"]>20]


# Create a box plot
plt.figure(figsize=(10, 6))
sns.boxplot(data=df, x='taskStatusLabel', y='weight')
plt.xlabel('Task Status Label')
plt.ylabel('Weight')
plt.title('Weight by Task Status Label')
plt.show()


df.groupby('taskStatusLabel')['weight'].quantile([0.25, 0.5, 0.75,0.98]).unstack()


df[df['weight'] > 100]


# Get the top 10 most frequently assigned tasks
top_10_assigned = df['taskAssignedTo'].value_counts().head(15)

# Create a bar plot
plt.figure(figsize=(10, 6))
sns.barplot(x=top_10_assigned.index, y=top_10_assigned.values)
plt.xlabel('Assigned To')
plt.ylabel('Count')
plt.title('Top 15 Most Frequently Assigned Tasks')
plt.xticks(rotation=45, ha='right')
plt.show()

df


# Get the top 10 most frequently assigned tasks for each task status label
top_10_assigned = df.groupby('taskStatusLabel')['taskAssignedTo'].value_counts().groupby(level=0).head(15).reset_index(name='count')

# Create a horizontal bar chart
plt.figure(figsize=(12, 8))
sns.barplot(data=top_10_assigned, x='count', y='taskAssignedTo', hue='taskStatusLabel', orient='horizontal', dodge=False)
plt.xlabel('Count')
plt.ylabel('Assigned To')
plt.title('Top 15 Most Frequently Assigned Tasks by Task Status Label')
plt.legend(title='Task Status Label')
plt.show()

df


df[(df['taskStatus']=="ongoing") ]


df[(df['taskStatus']=="ongoing") & (df['taskStatusLabel'].isna())]


import plotly.graph_objects as go

def heatmp(data,StatusLabel, top) :

    # Filter the data 
    filtered_df = data[data['taskStatusLabel'] == StatusLabel]

    # Get the top 10 taskAssignedTo values
    top_10_task_assigned = filtered_df['taskAssignedTo'].value_counts().nlargest(top)

    # Filter the data based on the top 10 taskAssignedTo values
    filtered_data = filtered_df[filtered_df['taskAssignedTo'].isin(top_10_task_assigned.index)]

    # Create a grouped count dataframe
    grouped_counts = filtered_data.groupby(['taskAssignedTo', 'taskDetailStatusLabel']).size().reset_index(name='count')

    # Create a trace for the heatmap
    trace = go.Heatmap(
        x=grouped_counts['taskAssignedTo'], #This represents the values to be displayed on the x-axis of the heatmap.
        y=grouped_counts['taskDetailStatusLabel'], #This represents the values to be displayed on the y-axis of the heatmap.
        z=grouped_counts['count'], #This represents the values that determine the color of each cell in the heatmap
        colorscale='Viridis',
        text=grouped_counts['count'],  # Show count in the heatmap cells
        # hoverinfo='text',
        hovertemplate='Task Assigned To: %{x}<br>Task Detail Status Label: %{y}<br>Count: %{z}', #Plotly allows you to customize the tooltip text that appears when hovering over data points in a plot
    )

    # Create the layout
    layout = go.Layout(
        title=f'Relationship between Task Assigned To and Task Detail Status Label where <b>Task Status Label is {StatusLabel}</b> (Top {top})',
        xaxis=dict(title='Task Assigned To'),
        yaxis=dict(title='Task Detail Status Label'),
    )

    # Create the figure
    fig = go.Figure(data=[trace], layout=layout)

    # Show the figure
    fig.show()


heatmp(df,"Failed",15)


heatmp(df,"Success",15)


# Count the occurrences of each brand origin
brand_origin_counts = df['branch_origin'].value_counts().head(10)

plt.figure(figsize=(12, 6))
sns.barplot(x=brand_origin_counts.index, y=brand_origin_counts.values)
plt.xticks(rotation=90)
plt.xlabel('Brand Origin')
plt.ylabel('Count')
plt.title('Count Top 10 of Brand Origins')

# Add count values as text annotations
for i, count in enumerate(brand_origin_counts.values):
    plt.text(i, count, str(count), ha='center', va='bottom')

plt.show()


# Get the top 10 cities based on count
top_10_cities = df['branch_origin'].value_counts().nlargest(10).index

# Filter the data based on the top 10 cities
filtered_df = df[df['branch_origin'].isin(top_10_cities)]

# Group the data by brand origin and task status label
grouped_df = filtered_df.groupby(['branch_origin', 'taskStatusLabel']).size().unstack(fill_value=0)

# Set the figure size
plt.figure(figsize=(20, 10))

# Create the stacked bar chart
sns.set_palette("viridis") # Set the color palette
grouped_df.plot(kind='bar', stacked=False)

# Set the x-axis label and rotate the tick labels for better visibility
plt.xlabel('Brand Origin')
plt.xticks(rotation=45)

# Set the y-axis label
plt.ylabel('Count')

# Set the plot title
plt.title('Relationship between Brand Origin and Task Status Label (Top 10 Cities)')

# Show the legend
plt.legend(title='Task Status Label')

# Display the plot
plt.show()

<Figure size 1440x720 with 0 Axes>


def heatmap_taskStat(taskLabel, column_analysis, top ):
    
    # Filter the data for taskStatusLabel is "Failed"
    filtered_df = df[df['taskStatusLabel'] == taskLabel]

    # Count the occurrences of each receiver city
    receiver_counts = filtered_df[column_analysis].value_counts().nlargest(top)

    # Filter the data based on the top 10 receiver cities
    filtered_df = filtered_df[filtered_df[column_analysis].isin(receiver_counts.index)]

    # Create a pivot table to get the count of taskDetailStatusLabel for each receiver city
    pivot_table = filtered_df.pivot_table(index=column_analysis, columns='taskDetailStatusLabel', values='taskId', aggfunc='count', fill_value=0)

    # Set the figure size
    plt.figure(figsize=(12, 6))

    # Create the heatmap using seaborn
    sns.heatmap(pivot_table, annot=True, fmt='d', cmap='YlOrRd')

    # Set the x-axis label
    plt.xlabel('Task Detail Status Label')

    # Set the y-axis label
    plt.ylabel('Receiver City')

    # Set the plot title
    plt.title(f'Relationship between {column_analysis} and Task Detail Status Label (Top {top} {column_analysis} - Failed Tasks)')

    # Show the plot
    plt.show()


heatmap_taskStat("Failed", "branch_origin",10)


heatmap_taskStat("Success", "branch_origin",10)


# Count the occurrences of each brand origin
brand_origin_counts = df['receiver_city'].value_counts().head(20)

# Create a bar plot
plt.figure(figsize=(12, 6))
sns.barplot(x=brand_origin_counts.index, y=brand_origin_counts.values, palette="Pastel1")
plt.xticks(rotation=90)
plt.xlabel('Brand Origin')
plt.ylabel('Count')
plt.title('Count Top 20 of Reciever City')

# Add count values as text annotations
for i, count in enumerate(brand_origin_counts.values):
    plt.text(i, count, str(count), ha='center', va='bottom')

plt.show()


import seaborn as sns
import matplotlib.pyplot as plt

# Count the occurrences of each receiver city
receiver_counts = df['receiver_city'].value_counts().nlargest(10)

# Filter the data based on the top 10 receiver cities
filtered_df = df[df['receiver_city'].isin(receiver_counts.index)]

# Set the figure size
plt.figure(figsize=(12, 6))

# Create a bar plot
ax = sns.countplot(x='receiver_city', hue='taskStatusLabel', data=filtered_df, palette="Pastel2")

# Set the x-axis label and rotate the tick labels for better visibility
plt.xlabel('Receiver City')
plt.xticks(rotation=90)

# Set the y-axis label
plt.ylabel('Count')

# Set the plot title
plt.title('Relationship between Receiver City and Task Status Label (Top 10 Cities)')

# Iterate over each bar to add the count value
for p in ax.patches:
    ax.annotate(format(p.get_height(), '.0f'), (p.get_x() + p.get_width() / 2., p.get_height()), ha = 'center', va = 'center', xytext = (0, 5), textcoords = 'offset points')

# Show the legend
plt.legend(title='Task Status Label')

# Display the plot
plt.show()


heatmap_taskStat("Failed", "receiver_city", 10)


heatmap_taskStat("Success", "receiver_city", 10)

df

	taskLocationDone	taskCreatedTime	cod	taskAssignedTo	taskCompletedTime	taskStatus	flow	taskId	UserVar
0	{'lon': 109.7629103, 'lat': -6.9266078}	2022-11-01 20:17:26 +0700	{'amount': 685000, 'received': True}	pacifiedLion0	2022-11-01 20:46:30 +0700	done	Delivery	4fe3b237c832ca4841a2	{'branch_dest': 'SRG', 'taskStatusLabel': 'Suc...
1	{'lon': 110.0339859, 'lat': -7.8761542}	2022-11-01 08:41:07 +0700	{'amount': 53500, 'received': True}	peacefulTacos6	2022-11-01 12:33:48 +0700	done	Delivery	08a4da25256affae8446	{'branch_dest': 'MGL', 'taskStatusLabel': 'Suc...
2	{'lon': 109.999733, 'lat': -7.8497772999999995}	2022-11-01 08:41:07 +0700	{'amount': 179500, 'received': True}	peacefulTacos6	2022-11-01 13:41:57 +0700	done	Delivery	2ff0dc469826158b7684	{'branch_dest': 'MGL', 'taskStatusLabel': 'Suc...
3	{'lon': 110.0037077, 'lat': -7.710998}	2022-11-01 08:41:07 +0700	{'amount': 31815, 'received': True}	peacefulTacos6	2022-11-01 18:18:19 +0700	done	Delivery	331c172c2b383f774328	{'branch_dest': 'MGL', 'taskStatusLabel': 'Suc...
4	{'lon': 110.0138869, 'lat': -7.8297416}	2022-11-01 08:41:07 +0700	{'amount': 144562, 'received': True}	peacefulTacos6	2022-11-01 10:51:49 +0700	done	Delivery	a9d53fa96c80baee8b23	{'branch_dest': 'MGL', 'taskStatusLabel': 'Suc...
...	...	...	...	...	...	...	...	...	...
8329	{'lon': 0, 'lat': 0}	2022-11-10 09:07:12 +0700	NaN	debonairPonie1	2022-11-10 09:38:04 +0700	done	Delivery	501af4e040a742e9e878	{'branch_dest': 'CGK', 'taskStatusLabel': 'Suc...
8330	{'lon': 110.3520537, 'lat': -7.8925713}	2022-11-10 09:21:42 +0700	NaN	murkyThrushe3	2022-11-10 09:37:52 +0700	done	Delivery	5cc952d9e9f8066dbf24	{'branch_dest': 'JOG', 'taskStatusLabel': 'Fai...
8331	{'lon': 105.6648975, 'lat': -5.3590626}	2022-11-10 09:36:44 +0700	NaN	enragedCake7	2022-11-10 09:37:55 +0700	done	Delivery	1b136b5a3c60749eb571	{'branch_dest': 'TKG', 'taskStatusLabel': 'Suc...
8332	{'lon': 119.8771726, 'lat': -8.5133055}	2022-11-10 07:25:40 +0700	{'amount': 151000, 'received': False}	lyingPaella2	2022-11-10 10:37:53 +0800	done	Delivery	e92e813c8539080c922e	{'branch_dest': 'KOE', 'taskStatusLabel': 'Fai...
8333	{'lon': 0, 'lat': 0}	2022-11-10 07:46:13 +0700	NaN	emptyPretzels3	2022-11-10 09:37:50 +0700	done	Delivery	cdb90c597655282306fd	{'branch_dest': 'CGK', 'taskStatusLabel': 'Suc...

	taskCreatedTime	taskAssignedTo	taskCompletedTime	taskStatus	flow	taskId	lon	lat	amount	received	branch_dest	taskStatusLabel	receiver_city	taskDetailStatusLabel	taskDetailStatus	weight	branch_origin	taskStatus
0	2022-11-01 20:17:26 +0700	pacifiedLion0	2022-11-01 20:46:30 +0700	done	Delivery	4fe3b237c832ca4841a2	109.762910	-6.926608	685000.0	True	SRG	Success	BATANG ,KAB BATANG	YANG BERSANGKUTAN	D01	13	CGK	COLF01
1	2022-11-01 08:41:07 +0700	peacefulTacos6	2022-11-01 12:33:48 +0700	done	Delivery	08a4da25256affae8446	110.033986	-7.876154	53500.0	True	MGL	Success	PURWODADI,PURWOREJO	YANG BERSANGKUTAN	D01	1.3	CGK	COLF01
2	2022-11-01 08:41:07 +0700	peacefulTacos6	2022-11-01 13:41:57 +0700	done	Delivery	2ff0dc469826158b7684	109.999733	-7.849777	179500.0	True	MGL	Success	PURWODADI,PURWOREJO	YANG BERSANGKUTAN	D01	3	CGK	COLF01
3	2022-11-01 08:41:07 +0700	peacefulTacos6	2022-11-01 18:18:19 +0700	done	Delivery	331c172c2b383f774328	110.003708	-7.710998	31815.0	True	MGL	Success	PURWODADI,PURWOREJO	YANG BERSANGKUTAN	D01	0.625	CGK	COLF01
4	2022-11-01 08:41:07 +0700	peacefulTacos6	2022-11-01 10:51:49 +0700	done	Delivery	a9d53fa96c80baee8b23	110.013887	-7.829742	144562.0	True	MGL	Success	BAGELEN,PURWOREJO	YANG BERSANGKUTAN	D01	3	CGK	COLF01
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8329	2022-11-10 09:07:12 +0700	debonairPonie1	2022-11-10 09:38:04 +0700	done	Delivery	501af4e040a742e9e878	0.000000	0.000000	NaN	NaN	CGK	Success	PALMERAH ,JAKARTA BA	ATASAN/STAFF/KARYAWAN/BAWAHAN	D10	1	CGK	COLF01
8330	2022-11-10 09:21:42 +0700	murkyThrushe3	2022-11-10 09:37:52 +0700	done	Delivery	5cc952d9e9f8066dbf24	110.352054	-7.892571	NaN	NaN	JOG	Failed	KOTA BANTUL	ALAMAT TIDAK LENGKAP service/ TIDAK DIKENAL	U01	1	TGR	COLF02
8331	2022-11-10 09:36:44 +0700	enragedCake7	2022-11-10 09:37:55 +0700	done	Delivery	1b136b5a3c60749eb571	105.664897	-5.359063	NaN	NaN	TKG	Success	MARGA SEKAMPUNG KAB.	YANG BERSANGKUTAN	D01	1.44	CGK	COLF01
8332	2022-11-10 07:25:40 +0700	lyingPaella2	2022-11-10 10:37:53 +0800	done	Delivery	e92e813c8539080c922e	119.877173	-8.513305	151000.0	False	KOE	Failed	KOMODO,LABUAN BAJO	PENERIMA PINDAH ALAMAT	U03	0.6	CGK	COLF02
8333	2022-11-10 07:46:13 +0700	emptyPretzels3	2022-11-10 09:37:50 +0700	done	Delivery	cdb90c597655282306fd	0.000000	0.000000	NaN	NaN	CGK	Success	JAKARTA PUSAT	RECEPTIONIST	D02	1	BPN	COLF01

	taskCreatedTime	taskAssignedTo	taskCompletedTime	taskStatus	flow	taskId	lon	lat	cod_amount	cod_received	branch_dest	taskStatusLabel	receiver_city	taskDetailStatusLabel	taskDetailStatus	weight	branch_origin	taskStatus_1
0	2022-11-01 20:17:26 +0700	pacifiedLion0	2022-11-01 20:46:30 +0700	done	Delivery	4fe3b237c832ca4841a2	109.762910	-6.926608	685000.0	True	SRG	Success	BATANG ,KAB BATANG	YANG BERSANGKUTAN	D01	13.000	CGK	COLF01
1	2022-11-01 08:41:07 +0700	peacefulTacos6	2022-11-01 12:33:48 +0700	done	Delivery	08a4da25256affae8446	110.033986	-7.876154	53500.0	True	MGL	Success	PURWODADI,PURWOREJO	YANG BERSANGKUTAN	D01	1.300	CGK	COLF01
2	2022-11-01 08:41:07 +0700	peacefulTacos6	2022-11-01 13:41:57 +0700	done	Delivery	2ff0dc469826158b7684	109.999733	-7.849777	179500.0	True	MGL	Success	PURWODADI,PURWOREJO	YANG BERSANGKUTAN	D01	3.000	CGK	COLF01
3	2022-11-01 08:41:07 +0700	peacefulTacos6	2022-11-01 18:18:19 +0700	done	Delivery	331c172c2b383f774328	110.003708	-7.710998	31815.0	True	MGL	Success	PURWODADI,PURWOREJO	YANG BERSANGKUTAN	D01	0.625	CGK	COLF01
4	2022-11-01 08:41:07 +0700	peacefulTacos6	2022-11-01 10:51:49 +0700	done	Delivery	a9d53fa96c80baee8b23	110.013887	-7.829742	144562.0	True	MGL	Success	BAGELEN,PURWOREJO	YANG BERSANGKUTAN	D01	3.000	CGK	COLF01
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8329	2022-11-10 09:07:12 +0700	debonairPonie1	2022-11-10 09:38:04 +0700	done	Delivery	501af4e040a742e9e878	0.000000	0.000000	NaN	NaN	CGK	Success	PALMERAH ,JAKARTA BA	ATASAN/STAFF/KARYAWAN/BAWAHAN	D10	1.000	CGK	COLF01
8330	2022-11-10 09:21:42 +0700	murkyThrushe3	2022-11-10 09:37:52 +0700	done	Delivery	5cc952d9e9f8066dbf24	110.352054	-7.892571	NaN	NaN	JOG	Failed	KOTA BANTUL	ALAMAT TIDAK LENGKAP service/ TIDAK DIKENAL	U01	1.000	TGR	COLF02
8331	2022-11-10 09:36:44 +0700	enragedCake7	2022-11-10 09:37:55 +0700	done	Delivery	1b136b5a3c60749eb571	105.664897	-5.359063	NaN	NaN	TKG	Success	MARGA SEKAMPUNG KAB.	YANG BERSANGKUTAN	D01	1.440	CGK	COLF01
8332	2022-11-10 07:25:40 +0700	lyingPaella2	2022-11-10 10:37:53 +0800	done	Delivery	e92e813c8539080c922e	119.877173	-8.513305	151000.0	False	KOE	Failed	KOMODO,LABUAN BAJO	PENERIMA PINDAH ALAMAT	U03	0.600	CGK	COLF02
8333	2022-11-10 07:46:13 +0700	emptyPretzels3	2022-11-10 09:37:50 +0700	done	Delivery	cdb90c597655282306fd	0.000000	0.000000	NaN	NaN	CGK	Success	JAKARTA PUSAT	RECEPTIONIST	D02	1.000	BPN	COLF01

	taskCreatedTime	taskAssignedTo	taskCompletedTime	taskStatus	flow	taskId	lon	lat	cod_amount	cod_received	branch_dest	taskStatusLabel	receiver_city	taskDetailStatusLabel	taskDetailStatus	weight	branch_origin	taskStatus_1
73	2022-11-01 09:31:32 +0700	enragedLlama3	2022-11-01 13:14:31 +0700	done	Delivery	b7f9f3f96def6b866166	98.814915	1.713917	NaN	NaN	DTB	Success	SARUDIK,TAPANULI TEN	YANG BERSANGKUTAN	D01	1.00	MES	COLF01
550	2022-11-01 15:04:06 +0700	mildCardinal0	NaN	done	Delivery	9779ae5b5f88ba0bf31c	NaN	NaN	NaN	NaN	BKI	Success	BABELAN,CIKARANG	YANG BERSANGKUTAN	D01	3.00	CGK	COLF01
551	2022-11-01 16:59:58 +0700	mildCardinal0	NaN	done	Delivery	8ade827b1e3530c02e82	NaN	NaN	NaN	NaN	BKI	Success	BABELAN,CIKARANG	YANG BERSANGKUTAN	D01	4.90	CGK	COLF01
552	2022-11-01 16:59:58 +0700	mildCardinal0	NaN	done	Delivery	292b1e9f73099dac1209	NaN	NaN	NaN	NaN	BKI	Success	BABELAN,CIKARANG	YANG BERSANGKUTAN	D01	8.00	CGK	COLF01
553	2022-11-01 16:59:58 +0700	mildCardinal0	NaN	done	Delivery	7e8862b3f4010465f006	NaN	NaN	NaN	NaN	BKI	Success	BABELAN,CIKARANG	YANG BERSANGKUTAN	D01	17.00	BDO	COLF01
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8328	2022-11-10 07:46:14 +0700	emptyPretzels3	2022-11-10 09:37:50 +0700	done	Delivery	469a6678ea253784e7e3	0.000000	0.000000	NaN	NaN	CGK	Success	JAKARTA PUSAT	RECEPTIONIST	D02	1.00	CGK	COLF01
8329	2022-11-10 09:07:12 +0700	debonairPonie1	2022-11-10 09:38:04 +0700	done	Delivery	501af4e040a742e9e878	0.000000	0.000000	NaN	NaN	CGK	Success	PALMERAH ,JAKARTA BA	ATASAN/STAFF/KARYAWAN/BAWAHAN	D10	1.00	CGK	COLF01
8330	2022-11-10 09:21:42 +0700	murkyThrushe3	2022-11-10 09:37:52 +0700	done	Delivery	5cc952d9e9f8066dbf24	110.352054	-7.892571	NaN	NaN	JOG	Failed	KOTA BANTUL	ALAMAT TIDAK LENGKAP service/ TIDAK DIKENAL	U01	1.00	TGR	COLF02
8331	2022-11-10 09:36:44 +0700	enragedCake7	2022-11-10 09:37:55 +0700	done	Delivery	1b136b5a3c60749eb571	105.664897	-5.359063	NaN	NaN	TKG	Success	MARGA SEKAMPUNG KAB.	YANG BERSANGKUTAN	D01	1.44	CGK	COLF01
8333	2022-11-10 07:46:13 +0700	emptyPretzels3	2022-11-10 09:37:50 +0700	done	Delivery	cdb90c597655282306fd	0.000000	0.000000	NaN	NaN	CGK	Success	JAKARTA PUSAT	RECEPTIONIST	D02	1.00	BPN	COLF01

	taskCreatedTime	taskAssignedTo	taskCompletedTime	taskStatus	flow	taskId	lon	lat	cod_amount	cod_received	branch_dest	taskStatusLabel	receiver_city	taskDetailStatusLabel	taskDetailStatus	weight	branch_origin	taskStatus_1
110	2022-11-01 08:33:37 +0700	grumpyCaribou3	2022-11-01 09:08:55 +0700	done	Delivery	5bc42ce849cb60659f02	97.817744	0.569078	16000.0	True	DTB	Success	TELUK DALAM,KAB.NIAS	YANG BERSANGKUTAN	D01	1.0	MES	COLF01
371	2022-11-01 07:24:05 +0700	importedStork6	2022-11-01 09:10:28 +0700	done	Delivery	1c8894b1383b870cab77	108.629415	-7.328136	16500.0	True	CXP	Success	WANAREJA,CILACAP	YANG BERSANGKUTAN	D01	0.3	CGK	COLF01
274	2022-11-01 08:31:33 +0700	cockyWhiting9	2022-11-01 09:12:05 +0700	done	Delivery	5fb3f23f8dcf16c6c2be	110.216317	-7.262263	93400.0	True	MGL	Success	KALORAN,TEMANGGUNG	YANG BERSANGKUTAN	D01	0.5	CGK	COLF01
566	2022-11-01 08:37:40 +0700	gleefulChough9	2022-11-01 09:14:20 +0700	done	Delivery	7e54cfd790164d1504df	110.270188	-6.971689	18999.0	True	SRG	Success	KALIWUNGU,KENDAL	YANG BERSANGKUTAN	D01	0.2	CGK	COLF01
272	2022-11-01 08:31:32 +0700	cockyWhiting9	2022-11-01 09:16:06 +0700	done	Delivery	3196869fc0ebd01db92e	110.224392	-7.262976	40633.0	True	MGL	Success	KALORAN,TEMANGGUNG	YANG BERSANGKUTAN	D01	1.3	CGK	COLF01
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
551	2022-11-01 16:59:58 +0700	mildCardinal0	NaN	done	Delivery	8ade827b1e3530c02e82	NaN	NaN	NaN	NaN	BKI	Success	BABELAN,CIKARANG	YANG BERSANGKUTAN	D01	4.9	CGK	COLF01
552	2022-11-01 16:59:58 +0700	mildCardinal0	NaN	done	Delivery	292b1e9f73099dac1209	NaN	NaN	NaN	NaN	BKI	Success	BABELAN,CIKARANG	YANG BERSANGKUTAN	D01	8.0	CGK	COLF01
553	2022-11-01 16:59:58 +0700	mildCardinal0	NaN	done	Delivery	7e8862b3f4010465f006	NaN	NaN	NaN	NaN	BKI	Success	BABELAN,CIKARANG	YANG BERSANGKUTAN	D01	17.0	BDO	COLF01
554	2022-11-01 10:13:02 +0700	mildCardinal0	NaN	done	Delivery	a4fb8aed363abc3d6af3	NaN	NaN	NaN	NaN	BKI	Success	BABELAN,CIKARANG	YANG BERSANGKUTAN	D01	1.0	CGK	COLF01
555	2022-11-01 10:13:03 +0700	mildCardinal0	NaN	done	Delivery	38e5d762840ec39984b1	NaN	NaN	NaN	NaN	BKI	Success	BABELAN,CIKARANG	YANG BERSANGKUTAN	D01	1.0	BTJ	COLF01

Data Preparation¶

Exploatory Data Analysis¶

Checking Missing¶

Visualization¶

1. Lot Lan¶

Lat Lon and task status¶

Lat Lon (0,0)¶

2. COD¶

COD Status¶

COD Status and Task Status Label Detail¶

COD Amount¶

COD Amount and Task Status¶

3. Task Status¶

Task status Label and Task Detail Status Label¶

Task status and COD¶

4. Weight¶

Task Assign to¶

Assigned To and Task Status Label¶

Assigned To and Task Status Detail Label by Task Status Label¶

Branch Origin¶

Branch and Task Status¶

Reciever CIty¶

Reciever city and Task Status Label¶

	taskCreatedTime	taskAssignedTo	taskCompletedTime	taskStatus	flow	taskId	lon	lat	cod_amount	cod_received	branch_dest	taskStatusLabel	receiver_city	taskDetailStatusLabel	taskDetailStatus	weight	branch_origin	taskStatus_1
680	2022-11-02 08:06:34 +0700	vengefulCheese4	NaN	ongoing	Delivery	0bed2d5cd82456af0239	NaN	NaN	NaN	NaN	MES	NaN	DOLOK PANRIBUAN,SIMA	NaN	NaN	1.00	CGK	NaN
684	2022-11-02 08:06:34 +0700	vengefulCheese4	NaN	ongoing	Delivery	3e9117e0d978e79e02e9	NaN	NaN	NaN	NaN	MES	NaN	SIANTAR TIMUR,PEMATA	NaN	NaN	1.00	MES	NaN
685	2022-11-02 08:06:34 +0700	vengefulCheese4	NaN	ongoing	Delivery	898eb4f04d5eb328ed94	NaN	NaN	550000.0	False	MES	NaN	SIANTAR TIMUR,PEMATA	NaN	NaN	1.00	SOC	NaN
686	2022-11-02 08:06:34 +0700	vengefulCheese4	NaN	ongoing	Delivery	eafd37c5536bc45e5a43	NaN	NaN	NaN	NaN	MES	NaN	SIANTAR TIMUR,PEMATA	NaN	NaN	1.00	MES	NaN
687	2022-11-02 08:06:34 +0700	vengefulCheese4	NaN	ongoing	Delivery	d59413b44eb9878eca66	NaN	NaN	NaN	NaN	MES	NaN	SIANTAR BARAT,PEMATA	NaN	NaN	1.00	PDG	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7954	2022-11-10 09:31:47 +0700	yearningOrange7	NaN	ongoing	Delivery	0247c7beef13f9d2fd7e	NaN	NaN	178000.0	False	BKI	NaN	BABELAN,CIKARANG	NaN	NaN	1.00	CGK	NaN
7992	2022-11-10 09:31:53 +0700	relievedIguana0	NaN	ongoing	Delivery	abe2c1babb7feef85fda	NaN	NaN	275010.0	False	MDC	NaN	MELONGUANE	NaN	NaN	1.00	CGK	NaN
8002	2022-11-10 09:32:08 +0700	mellowPolenta1	NaN	ongoing	Delivery	d6ce7905450324498c30	NaN	NaN	NaN	NaN	DPK	NaN	CINERE,DEPOK	NaN	NaN	4.35	CGK	NaN
8007	2022-11-10 09:32:07 +0700	pacifiedThrush4	NaN	ongoing	Delivery	d3db332e6196498e781d	NaN	NaN	269000.0	False	PDG	NaN	GUNUNG TALANG,AROSUK	NaN	NaN	1.00	CGK	NaN
8077	2022-11-10 08:58:14 +0700	scornfulAntelope4	NaN	ongoing	Delivery	85825f68ea063eabbc68	NaN	NaN	NaN	NaN	BPN	NaN	PENAJAM, PENAJAM PAS	NaN	NaN	1.00	CGK	NaN

	taskCreatedTime	taskAssignedTo	taskCompletedTime	taskStatus	flow	taskId	lon	lat	cod_amount	cod_received	branch_dest	taskStatusLabel	receiver_city	taskDetailStatusLabel	taskDetailStatus	weight	branch_origin	taskStatus_1
1823	2022-11-03 07:23:46 +0700	murkyEggs4	2022-11-03 08:02:25 +0700	done	Delivery	1caf60bc1fdb4c75f38a	0.0	0.0	NaN	NaN	SRG	Success	KAJEN,KAB.PEKALONGAN	RECEPTIONIST	D02	4.00	CGK	COLF01
2403	2022-11-03 07:23:46 +0700	murkyEggs4	2022-11-03 08:22:08 +0700	done	Delivery	627beb98b700cce025f3	0.0	0.0	NaN	NaN	SRG	Success	KAJEN,KAB.PEKALONGAN	RECEPTIONIST	D02	1.00	BOO	COLF01
2488	2022-11-03 07:04:32 +0700	innocentDinosaur7	2022-11-03 09:19:50 +0800	done	Delivery	db9bdc965dfb5725fd54	0.0	0.0	NaN	NaN	KOE	Success	None	YANG BERSANGKUTAN	D01	1.00	KOE	COLF01
2508	2022-11-04 10:12:06 +0700	debonairBustard8	2022-11-04 13:57:49 +0700	done	Delivery	3caed72cf0173f1d729f	0.0	0.0	NaN	NaN	CGK	Success	JAKARTA BARAT	KELUARGA/SAUDARA	D09	0.50	CGK	COLF01
2518	2022-11-04 11:05:38 +0700	boastfulPaella0	2022-11-04 13:57:47 +0700	done	Delivery	291df3ba4dc004885502	0.0	0.0	NaN	NaN	CGK	Success	KELAPA GADING ,JAKAR	RECEPTIONIST	D02	1.00	BKI	COLF01
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8325	2022-11-10 08:23:41 +0700	ferventBoa6	2022-11-10 09:38:02 +0700	done	Delivery	a51606c83f1f0d64fbdb	0.0	0.0	NaN	NaN	DJB	Success	DENDANG,MUARASABAK	YANG BERSANGKUTAN	D01	2.00	CGK	COLF01
8326	2022-11-10 08:37:54 +0700	gutturalLapwing2	2022-11-10 09:38:03 +0700	done	Delivery	ae9cb5cfa113db947621	0.0	0.0	NaN	NaN	DPK	Success	SUKMAJAYA,DEPOK	YANG BERSANGKUTAN	D01	4.08	CGK	COLF01
8328	2022-11-10 07:46:14 +0700	emptyPretzels3	2022-11-10 09:37:50 +0700	done	Delivery	469a6678ea253784e7e3	0.0	0.0	NaN	NaN	CGK	Success	JAKARTA PUSAT	RECEPTIONIST	D02	1.00	CGK	COLF01
8329	2022-11-10 09:07:12 +0700	debonairPonie1	2022-11-10 09:38:04 +0700	done	Delivery	501af4e040a742e9e878	0.0	0.0	NaN	NaN	CGK	Success	PALMERAH ,JAKARTA BA	ATASAN/STAFF/KARYAWAN/BAWAHAN	D10	1.00	CGK	COLF01
8333	2022-11-10 07:46:13 +0700	emptyPretzels3	2022-11-10 09:37:50 +0700	done	Delivery	cdb90c597655282306fd	0.0	0.0	NaN	NaN	CGK	Success	JAKARTA PUSAT	RECEPTIONIST	D02	1.00	BPN	COLF01

	0.25	0.50	0.75	0.98
taskStatusLabel
Failed	71563.0	158000.0	232975.0	757670.0
Success	39670.5	93400.0	182875.0	615100.0

	taskCreatedTime	taskAssignedTo	taskCompletedTime	taskStatus	flow	taskId	lon	lat	cod_amount	cod_received	branch_dest	taskStatusLabel	receiver_city	taskDetailStatusLabel	taskDetailStatus	weight	branch_origin	taskStatus_1
165	2022-11-01 12:31:12 +0700	somberOcelot3	2022-11-01 13:37:49 +0700	done	Delivery	b5cabeeeab088d6b50a9	107.449274	-6.946676	610000.0	True	BDO	Success	CILILIN,NGAMPRAH	YANG BERSANGKUTAN	D01	31.208	CGK	COLF01
869	2022-11-02 08:08:52 +0700	spiritedCardinal2	2022-11-02 08:10:29 +0700	done	Delivery	0d7901bd53eba331f04a	103.847116	-2.893538	NaN	No applicable	PLM	Failed	SEKAYU,KAB.MUSI BANY	RUMAH service/ KANTOR KOSONG (MASIH DIHUNI)	U05	48.048	CGK	COLF02
911	2022-11-02 08:09:52 +0700	artisticPolenta8	2022-11-02 08:10:29 +0700	done	Delivery	2ca763286f99e0d1e89c	103.847110	-2.893539	NaN	No applicable	PLM	Failed	SEKAYU,KAB.MUSI BANY	DIAMBIL SENDIRI	CR3	116.000	CGK	COLF02
981	2022-11-02 08:12:50 +0700	eagerThrush5	2022-11-02 08:13:16 +0700	done	Delivery	b4cf538dec8f1fe4ad67	104.112002	-2.593567	NaN	No applicable	PLM	Success	SUNGAI LILIN,SEKAYU	YANG BERSANGKUTAN	D01	20.500	CGK	COLF01
1149	2022-11-02 07:37:45 +0700	curiousPonie1	2022-11-02 08:38:36 +0800	done	Delivery	04a419a84159d8b26ccb	114.661971	-3.447025	NaN	No applicable	BDJ	Success	BANJARBARU	ATASAN/STAFF/KARYAWAN/BAWAHAN	D10	21.839	CGK	COLF01
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8061	2022-11-10 09:15:13 +0700	wakefulDoves2	2022-11-10 10:32:11 +0800	done	Delivery	449bbfb7ab8ce9ca2fdc	0.000000	0.000000	NaN	No applicable	SMD	Success	SAMARINDA ULU	YANG BERSANGKUTAN	D01	22.500	CGK	COLF01
8157	2022-11-10 07:33:05 +0700	pleasedBoa3	2022-11-10 08:57:00 +0700	done	Delivery	2310d35f82c6167e1948	0.000000	0.000000	NaN	No applicable	MJK	Success	NGANJUK,KAB. NGANJUK	YANG BERSANGKUTAN	D01	20.020	CGK	COLF01
8190	2022-11-10 08:54:32 +0700	finickyPie7	2022-11-10 08:57:04 +0700	done	Delivery	cc4ccd18924d88c12215	111.679438	-8.079674	4553000.0	False	SUB	Failed	PULE, TRENGGALEK	PENERIMA MENOLAK BAYAR (KIRIMAN COD)	U08	62.160	CGK	COLF02
8229	2022-11-10 09:34:45 +0700	boredEggs2	2022-11-10 09:37:51 +0700	done	Delivery	3f2f36385bb61e75d56d	104.478694	0.894101	NaN	No applicable	TNJ	Success	BUKIT BESTARI, TANJU	YANG BERSANGKUTAN	D01	63.000	CGK	COLF01
8321	2022-11-10 08:13:30 +0700	humorousPiglet8	2022-11-10 09:38:02 +0700	done	Delivery	85f340c19c6cffd3135e	107.694447	-6.924457	NaN	No applicable	BDO	Success	UJUNGBERUNG, BANDUNG	YANG BERSANGKUTAN	D01	54.800	CGK	COLF01